AI data centers and models have drastic carbon footprints.

Today data centers run 24/7 and most derive their energy from fossil fuels, although there are increasing efforts to use renewable energy resources. Because of the energy the world’s data centers consume, they account for 2.5 to 3.7 percent of global greenhouse gas emissions, exceeding even those of the aviation industry. A data center can have thousands of computers or more. Photo: Sivaserver Most of a data center’s energy is used to operate processors and chips. Like other computer systems, AI systems process information using zeros and ones. Every time a bit—the smallest amount of data computers can process—changes its state between one and zero, it consumes a small amount of electricity and generates heat. Because servers must be kept cool to function, around 40 percent of the electricity data centers use goes towards massive air conditioners. Without them, servers would overheat and fail. In 2021, global data center electricity use was about 0.9 to 1.3 percent of global electricity demand. One study estimated it could increase to 1.86 percent by 2030. As the capabilities and complexity of AI models rapidly increase over the next few years, their processing and energy consumption needs will too. One research company predicted that by 2028, there will be a four-fold improvement in computing performance, and a 50-fold increase in processing workloads due to increased use, more demanding queries, and more sophisticated models with many more parameters. It’s estimated that the energy consumption of data centers on the European continent will grow 28 percent by 2030. Moore’s Law charts the continuing increase in computing power. Each dot represents an attempt to build the best computer of the day. Photo: Steve Jurvetson With AI already being integrated into search engines like Bing and Bard, more computing power is needed to train and run models. Experts say this could increase the computing power needed—as well as the energy used—by up to five times per search. Moreover, AI models need to be continually retrained to keep up to date with current information. Training In 2019, University of Massachusetts Amherst researchers trained several large language models and found that training a single AI model can emit over 626,000 pounds of CO2, equivalent to the emissions of five cars over their lifetimes. A more recent study reported that training GPT-3 with 175 billion parameters consumed 1287 MWh of electricity, and resulted in carbon emissions of 502 metric tons of carbon, equivalent to driving 112 gasoline powered cars for a year. Inference Once models are deployed, inference—the mode where the AI makes predictions about new data and responds to queries—may consume even more energy than training. Google estimated that of the energy used in AI for training and inference, 60 percent goes towards inference, and 40 percent for training. GPT-3’s daily carbon footprint was been estimated to be equivalent to 50 pounds of CO2 or 8.4 tons of CO2 in a year. Inference energy consumption is high because while training is usually done multiple times to keep models current and optimized, inference is used many many times to serve millions of users. Two months after its launch, ChatGPT had 100 million active users. Instead of employing existing web searches that rely on smaller AI models, many people are eager to use AI for everything, but a single request in ChatGPT can consume 100 times more energy than one Google search, according to one tech expert. Northeastern University and MIT researchers estimated that inference consumes more energy than training, but there is still debate over which mode is the greater energy consumer. What is certain, though, is that as OpenAI, Google, Microsoft, and the Chinese search company Baidu compete to create larger, more sophisticated models, and as more people use them, their carbon footprints will grow. This could potentially make decarbonizing our societies much more difficult. “If you look at the history of computational advances, I think we’re in the ‘amazed by what we can do, this is great, let’s do it phase,’” said Clifford Stein, interim director of Columbia University’s Data Science Institute, and professor of computer science at the Fu Foundation School of Engineering and Applied Science. “But we should be coming to a phase where we’re aware of the energy usage and taking that into our calculations of whether we should or shouldn’t be doing it, or how big the model should be. We should be developing the tools to think about if it’s worth using these large language models given how much energy they’re consuming, and at least be aware of their energy and environmental costs.”